Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
This addresses the problem of real-time power system operations by enabling scalable and adaptable machine learning solutions, though it is incremental in improving generalization over prior methods.
The paper tackles the computational expense of AC Optimal Power Flow (ACOPF) for large-scale power systems by proposing a Hybrid Heterogeneous Message Passing Neural Network (HH-MPNN), which achieves less than 1% optimality gap on default topologies and less than 3% gap on unseen topologies with computational speedups of 1,000x to 10,000x compared to conventional solvers.
AC Optimal Power Flow (ACOPF) is computationally expensive for large-scale power systems, with conventional solvers requiring prohibitive solution times. Machine learning approaches offer computational speedups but struggle with scalability and topology adaptability without expensive retraining. To enable scalability across grid sizes and adaptability to topology changes, we propose a Hybrid Heterogeneous Message Passing Neural Network (HH-MPNN). HH-MPNN models buses, generators, loads, shunts, transmission lines and transformers as distinct node or edge types, combined with a scalable transformer model for handling long-range dependencies. On grids from 14 to 2,000 buses, HH-MPNN achieves less than 1% optimality gap on default topologies. Applied zero-shot to thousands of unseen topologies, HH-MPNN achieves less than 3% optimality gap despite training only on default topologies. Pre-training on smaller grids also improves results on a larger grid. Computational speedups reach 1,000x to 10,000x compared to interior point solvers. These results advance practical, generalizable machine learning for real-time power system operations.